The University of Texas MD Anderson Cancer Center
Department of Bioinformatics and Computational Biology
Professor Wenyi Wang is a big data scientist with an academic background in both statistics and biology. Her main interests lie in using data wrangling to understand big data generated by cancer multi-omics. Her lab’s statistical methodology and tool development is data-driven and motivated by solving important and novel biological questions. The lab’s current major research focuses are:
1) Tumor heterogeneity and evolution using computational deconvolution of both transcriptomic and genomic data (inter-gromics), from both bulk sample and single cell sequencing data. Our goal is to understand a) tumor microenvironment that may lead to different outcomes in prognosis or response to treatment, b) the molecular mechanism of cell-cell interactions in normal tissues.
2) Personalized cancer risk prediction models, using TP53 mutation-associated Li-Fraumeni syndrome as a disease model; and novel statistical modeling and study design to further our understanding of the pan-cancer impact of TP53 mutations.
The lab uses statistical/computational toolbox as needed, including deep learning approaches. Our current focus is on mixture modeling and machine learning in high dimensional data.
See full publication list here: https://odin.mdacc.tmc.edu/~wwang7/publications.html, and the list of software tools at http://github.com/wwylab/.
A tutorial in the lab would involve domain knowledge learning as well as methods or software tool development. Prospective students must have had training in statistics/biostatistics; strong programming skills, in particular R/Python and preferably one lower level computer language such as C; and interest in statistical methodology research. Expertise or skills in any of the following areas are desirable: next-generation sequencing data analysis and cancer biology.
Education & Training
Ph.D. - Johns Hopkins Bloomberg School of Public Health - 2007